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Nuri Taufiq
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Pendekatan Model Machine Learning dalam Pemeringkatan Status Sosial Ekonomi Rumah Tangga di Indonesia Nuri Taufiq; Siti Mariyah
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (535.002 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1018

Abstract

The method used for ranking the socioeconomic status of households in the Integrated Database is to predict the value of household expenditures using the Proxy Mean Testing (PMT) method. In general, this method is a predictive model using a regression technique. The choice of statistical model used is forward-stepwise. In practice it is assumed that the predictor variables used in PMT have a linear correlation with the expenditure variable. This study tries to apply a machine learning approach as an alternative prediction method other than the forward-stepwise model. The model is built using several machine learning algorithms such as Multivariate Adaptive Regression Splines (MARS), K-Nearest Neighbors, Decision Tree, and Bagging. The results show that the machine learning model produces an average inclusion error (IE) value that is lower than the average exclusion error (EE) value. Machine learning model works effectively in reducing IE but is not sensitive enough to reduce EE. The average value of IE machine learning model is 0.21 while the average value of IE PMT model is 0.29.
Penciri Kemiskinan Ekstrem di 35 Kabupaten Prioritas Penanganan Kemiskinan Ekstrem Nuri Taufiq
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (322.537 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1258

Abstract

The government is currently targeting extreme poverty by 2024 to reach 0 percent. This achievement target is 6 years faster than the SDGs target. In 2021, extreme poverty alleviation efforts will be focused on 7 provinces and prioritized on 35 districts. Efforts are needed to accelerate the handling of extreme poverty, one of which is to sharpen the database to achieve target accuracy. Recognizing how the characteristics of extreme poor households are an important part of efforts to improve databases for targeting, especially in minimizing exclusion errors. By using Chi-square Automatic Interaction Detection (CHAID) in 35 priority districts for handling extreme poverty, this study finds that the dependency ratio on households is the predictor variable that has the strongest interaction with extreme poverty status. Furthermore, the estimation results using the ordinal logistic regression model confirmed that the dependency ratio had a statistically significant effect on extreme poverty with a marginal effect value of 0.072. An additional one point in the value of the dependency ratio will increase the chance of experiencing extreme poverty by 7.2 percentage points
Co-Authors Siti Mariyah